Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
Recently, the CNNs have been popular in an autonomous vehicle industry. When used in the autonomous vehicle industry, the CNNs perform functions of acquiring images from a camera installed on a vehicle, searching for lanes, etc.
However, if the CNNs only use videos from the camera, in a certain situation, i.e., a situation where reliability of the videos is not quite good due to foggy or dark weather, safety of autonomous driving cannot be guaranteed. Therefore, use of additional sensors, e.g., at least one radar and/or at least one LiDAR, other than cameras helps to make the autonomous driving safer, however, conventional arts only use them in a simple two-track fashion and just perform separate calculations of information from the cameras and information from the radar and/or the LiDAR for use.
In this case, each of neural networks operates independently, thus inefficiently.